Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Usage of polystyrene disposable food dishes in the lightweight concrete making

The increasing use and subsequent accumulation of polystyrene containers has triggered a substantial environmental problem. This study investigated using varied percentages of solid waste polystyrene disposable food dishes in the production of lightweight concrete samples with 350 kilograms per cubic meter (kg/m³) of cement and a density of 1,300 kg/m³. The polystyrene disposable dishes were ground into beads of 0–3 millimeters (mm) and 3–6 mm in size. First, the characteristics of Type II Portland cement, polystyrene, and aggregates were examined. The following characteristics of concrete using ASTM International and British Standards Institution standards were tested: slump, compressive strength, ability to resist chloride ion penetration, and resistance of concrete to rapid freezing and thawing cycles. Scanning electron microscopy (SEM) and energy dispersive X‐ray spectroscopy analytical techniques were also used. The slump of samples varied between 40 and 70 mm and was not dependent on either the polystyrene percentage or the size of the polystyrene beads in the concrete samples (p‐value > .05). The compressive strength of the concrete samples after 90 days of curing, and using different percentages of polystyrene, varied between 96 and 113 kilograms per square centimeter (kg/cm²). The resistance of the samples to the freezing and thawing cycle and chloride ion penetration were affected unfavorably by the presence of the polystyrene. The SEM technique indicated that concrete samples containing 15% and 25% polystyrene had denser crystals and less void than concrete samples with 40% and 55% polystyrene.     

Comparison of energy produced from waste by removing its different components: A case study of the waste landfill in Tehran

In developing and populated cities such as Tehran, a massive amount of municipal solid waste (MSW), both wet and dry, is transferred to landfills daily. Combustion is one of the most common methods of using mixed waste energy from the past to the present. The Dulong formula is widely used to calculate the energy released from MSW combustion. According to the constituent components of Tehran MSW, removing food waste leads to an increase in energy potential, which will be a suitable condition for energy production. In this work, the energy derived from the combustion of mixed and separated dry MSW generated in Tehran was calculated using the experimental Dulong formula and tables in Integrated Solid Waste Management (Tchobanoglous et al. 1993, McGraw‐Hill). The Dulong formula indicates that the use of Tehran mixed MSW (without separating materials for recycling) as a fuel source yields 8,966.40 KJ/kg while the use of Tchobanoglous et al. (1993) tables can generate 8,236 kJ/kg. By removing food waste and recyclable materials, the potential of energy production changes to 22,047 kJ/kg using the Dulong formula and 16,207 kJ/kg and the Tchobanoglous et al. (1993) tables. It indicates increase by 1.46 times and 46%, respectively. Regarding the 200‐ton capacity of the Tehran waste incinerator, the Dulong formula indicated generation of 4,409 MJ/day energy, and the Tchobanoglous et al. table presents 3,241 MJ/day. Therefore, considering that Tehran generates more than 4,000 tons of reject waste daily, it can easily be converted to energy rather than landfilled. This can alleviate the problem of buying land and construction of landfills and leachate generation.     

Environmental life cycle assessment of different types of the municipal wastewater pipeline network

With the onset of social life, humans have considered waste disposal as essential, and they have been able to repel it through brick and clay channels. Checking sewage pipes for energy consumption and a longer lifetime than other sewage system components is important. Climate change and exploitation of industrial resources have made environmental impacts, which are important factors in decision making. The purpose of this study was to introduce the most suitable type of sewage pipe considering environmental protection. Therefore, we applied the environmental life cycle assessment (LCA) method, using Sima Pro 8.2.3 software for the one-kilometer length of concrete pipes (300 mm in diameter), Polyvinyl chloride (PVC), and polyethylene (PE) (315 mm in diameter). Also, the BEES method and sensitivity analysis were used to validate the results. The comparison between three types of municipal wastewater pipes indicated that PE pipes are a more environmentally friendly option than PVC, and concrete pipes in pipe recycling, reducing extraction from untapped resources, and inefficient extraction of resources. Electricity, diesel fuel, and sulfate resistance cement consumption for concrete production are the most pollution elements in the LCA of concrete pipes. Usage of PVC granular, sanitary landfill of PVC pipes, and using hydraulic drill in LCA of PVC pipes are the most elements of generating pollution. The usage of PE granules, PE pipes landfilling, hydraulic excavator, and electricity consumption in the LCA of the PE pipes are the greatest polluting parameters.     

Life cycle assessment of clinker production using refuse‐derived fuel: A case study using refuse‐derived fuel from Tehran municipal solid waste

One of the techniques used to dispose of 4,000 tons per day (TPD) of non‐recyclable waste from Tehran is to burn it as an alternative fuel in cement kilns. This practice reduces emissions from landfills, prevents the loss of waste energy, and conserves fossil fuel resources. The aim of our study was to conduct a life cycle assessment (LCA) of clinker production in cement kilns using a combination of natural gas, mazut, a form of heavy, low‐quality fuel oil, and refuse‐derived fuel (RDF) from Tehran. We used SimaPro 7.1 software to perform an LCA of 1 kilogram (kg) of clinker produced using the following fuel combinations: the first scenario involved natural gas consumption alone, the second scenario involved a combination of natural gas and mazut, with the mazut providing 5% to 30% of the heating value needed to produce cement clinker in the kiln, and the third scenario involved a combination of natural gas and RDF (providing 5% to 30% of the heating needed in the kiln). The impact categories in the LCA of global warming, eutrophication, and acidification were assessed by the Center of Environmental Science of Leiden University (Centrum voor Milieukunde Leiden—CML) CML 2000 method. The results indicated that the third scenario, involving natural gas and RDF, reduced acidification by 2.14–11.5% and global warming by 0–1.3% relative to the first scenario involving the use of only natural gas. In addition, we observed a 0.65–3.81% reduction in acidification and a 0.9–3.8% reduction in global warming under the third scenario compared with the second scenario (co‐firing of natural gas and mazut). The amount of nitrogen oxides (NO_X) emitted from the combustion of the Tehran RDF was greater than that was emitted when burning mazut. Therefore, reduction of nitrogen from the RDF composition is necessary. This study indicates that the use of Tehran RDF (with reduced nitrogen) in Tehran cement kilns does not increase cement kiln NO_X, sulfur dioxide (SO₂), and carbon dioxide (CO₂) emissions; however, we need to conduct additional investigation into the chemical composition of the Tehran waste before using solid waste in place of fossil fuels.     

The Comparison of Lagrangian and Gaussian Models in Predicting of Air Pollution Emission Using Experimental Study,a Case Study: Ammonia Emission

Ammonia, as a colorless gas with a sharp odor, is considered as one of the created odors in the composting and landfill of solid waste. We used experimental data to study the robustness of AERMOD and the forward Lagrangian stochastic (FLS) in predicting ammonia emission in short range. The study area was Kahrizak landfill and composting plants, Tehran, Iran. The boundary layer parameters for the FLS were calculated on the basis of mean values of temperature, wind speed, and direction. While, the boundary layers of AERMOD were computed on the basis of exact meteorological data. The results depicted that AERMOD prediction at distances less than 1000 m from the sources and the locations inside the sources were poor. However, the results of FLS indicated more agreement with the field measurement, which the coefficient of determination was 0.83. Both models predicted, in the distance of 2000 m from the source, the ammonia concentration would be insignificant.     

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca²⁺), magnesium ions (Mg²⁺), sodium ions (Na⁺), chloride ions (Cl^?), sulfate (), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R ²) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R ² between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.     

Predicting Particulate Matter (PM2.5) Concentrations in the Air of Shahr‐e Ray City,Iran, by Using an Artificial Neural Network

Particulate matter (PM), along with other air pollutants, pose serious hazards to human health. The Artificial Neural Network (ANN) is a branch of artificial intelligence that has an ability to make accurate predictions. In this article, the authors describe such methods and how historical data on air quality, moisture, wind velocity, and temperature in Shahr‐e Ray City, located at the southern tip of Tehran, was used to train an ANN to provide accurate predictions of PM concentrations. The availability of such predictions can offer significant assistance to those who are working to reduce air pollution.